Cash application · October 2026 Edition
BILL vs HighRadius
Zero of fourteen models named BILL first on the direct prompt; three named HighRadius. BILL was named by ten of the fourteen models and HighRadius by fourteen and BILL carries 13 labels and HighRadius 51, so the shares are not directly comparable.
Named in fifteen categories this edition.
Named in twenty-two categories this edition.
First-choice share9%9%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate8%39%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#4A position in a field of 14; printed, not drawn.
Labels1351A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, BILL reading right to left. Rank and label count are printed, not drawn.Versapay was named alongside these two in eight of the fourteen direct answers. Versapay vs BILL · Versapay vs HighRadius · Upflow vs BILL
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the cash application page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
BILLFirst choices, of fourteen modelsHighRadius
Direct032 against HighRadius
Paraphrase311 against BILL · 4 against HighRadius
Comparative010
Budget-constrained104 against HighRadius
Scale-constrained002 against HighRadius
Negative008 against HighRadius
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where BILL and HighRadius stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | ||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | |||
| Grok 4.1 Fast | | | | | | |
| Mistral Small | | | | | ||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | | |
| MiniMax M2.5 | | |||||
| GPT-6 Luna | | | | |||
| Muse Glimmer 30B | | | | |
BILL HighRadius first choice named as an alternative argued againstblank: not namedEach cell is one answer, BILL on the left and HighRadius on the right.The direct prompt
The plain question, one answer per model, grouped by where BILL and HighRadius stood in it.
HighRadius first, BILL not the choice
3 of 14 modelsBILL was named in the answer but not as the choice, or not at all.Claude Haiku 4.5HighRadius alternatives: Billtrust, Quadient Accounts Receivable, Versapay
Perplexity SonarHighRadius alternatives: Chaser, Rex, Upflow, Versapay
GLM 4.7 FlashXHighRadius alternatives: Billtrust, Esker, Serrala, Stuut, Versapay
Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Gemini 3.5 FlashVersapay alternatives: Cashbook, HighRadius, Quadient Accounts Receivable, Upflow
Grok 4.1 Fastno first choice alternatives: Billtrust, HighRadius, Quadient Accounts Receivable, Stuut, Versapay
Kimi K2Stuut alternatives: HighRadius, Quadient Accounts Receivable, Versapay
GPT-6 LunaBilltrust alternatives: Esker, HighRadius
Neither was named
7 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniBilltrust Unified AR alternatives: BlackLine Invoice-to-Cash, HighRadius Accounts Receivable Software
Mistral SmallStuut alternatives: Versapay
DeepSeek V4 FlashStuut alternatives: Billtrust, Monk, SINGOA, Versapay
Llama 4 MaverickMonk alternatives: Cashbook, Gaviti, Upflow
Qwen 3.7 FlashGlid alternatives: BlackLine Cash Application, Tipalti, Workiva
MiniMax M2.5no first choice
Muse Glimmer 30Bno first choice alternatives: NetSuite’s built-in cash application module
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Mid-marketThe figures above
Level: the same share of first choices.
BILL9%#3 of 14HighRadius9%#4 of 14Enterprise
The order flips: HighRadius leads at enterprise.
HighRadius62%#1 of 8BILL0%#– of 8What the models said about BILL
No label in this category carried a quote.
What the models said about HighRadius
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“"Avoid HighRadius, Esker, and most of Billtrust/BlackLine unless your payment volume or audit requirements genuinely justify enterprise pricing."” Muse Glimmer 30B · negative prompt · hard negative
“Some sources advise negotiating carefully or avoiding standard contracts with HighRadius, especially for smaller businesses” Mistral Small · negative prompt · hard negative
“Several sources explicitly suggest negotiating carefully or avoiding standard contracts from HighRadius.” DeepSeek V4 Flash · negative prompt · hard negative
“For mid-market organizations, HighRadius delivers out-of-the-box automation to unify cash application... achieving up to 90%+ touchless cash application” Claude Haiku 4.5 · direct prompt · first choice
“Leader in Gartner Magic Quadrant for Invoice-to-Cash (3x recognized); best for mid-market to enterprise with high-volume, complex global AR” Grok 4.1 Fast · comparative prompt · first choice
“the strongest default choice is usually HighRadius if your priority is *high-volume, complex cash application with automation depth*” Perplexity Sonar · direct prompt · first choice
Also compared
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.